normalize

It is generally very important to normalize the data matrix before
fitting a neural network model in keras.

Provides a consistent interface to the 'Keras' Deep Learning Library
directly from within R. 'Keras' provides specifications for describing dense
neural networks, convolution neural networks (CNN) and recurrent neural networks
(RNN) running on top of either 'TensorFlow' or 'Theano'. Type conversions between
Python and R are automatically handled correctly, even when the default
choices would otherwise lead to errors. Includes complete R documentation
and many working examples.

Taylor Arnold

kerasR

R Interface to the Keras Deep Learning Library

Andrie de Vries

normalize function

<dl><dt>x</dt>
<dd>Numpy array to normalize</dd>
<dt>axis</dt>
<dd>axis along which to normalize. (starts at 0). -1</dd>
<dt>order</dt>
<dd>Normalization order (e.g. 2 for L2 norm).</dd></dl>

Arguments

Taylor B. Arnold, <a href="/link/taylor.arnold%40acm.org?package=kerasR&version=0.8.1" data-mini-rdoc="kerasR::taylor.arnold@acm.org">taylor.arnold@acm.org</a>

Author

Normalize a Numpy array. — normalize

<dl>

<dt>x</dt>
<dd>Numpy array to normalize</dd>


<dt>axis</dt>
<dd>axis along which to normalize. (starts at 0). -1</dd>


<dt>order</dt>
<dd>Normalization order (e.g. 2 for L2 norm).</dd>

</dl>

Taylor B. Arnold, <a href='mailto:taylor.arnold@acm.org'>taylor.arnold@acm.org</a>

normalize: Normalize a Numpy array.

Description

Usage

Arguments

Author

References